Minimum track record length

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond the annotations (readOnlyHint=false, destructiveHint=false, openWorldHint=true), the description adds a meaningful interpretive boundary: the result is not admission and is not a forecast. It also discloses that the output spans both a minimum track record length and a probabilistic Sharpe, which helps an agent understand what the tool actually produces. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded with the core calculation, and the second sentence adds an important caveat without much bloat. The phrasing 'above or below any threshold is not admission to anything' is slightly cryptic belEver, so it is not perfectly clear.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and 7 parameters, an agent is left to infer the exact return shape and units beyond 'observations and years.' The description captures the main inputs and purpose but does not state the output format or how the validation result is represented. This is adequate but not complete for a moderately complex statistical tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already documents every parameter. The description adds relational context by tying observed Sharpe to a benchmark at a confidence level and connecting observations to probabilistic Sharpe, but it does not describe individual fields beyond what the schema already provides. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states that the tool computes the minimum track record length—in observations and years—needed for an observed Sharpe to beat a benchmark at a confidence level, and also the probabilistic Sharpe of the current record. This clearly identifies the tool's statistical purpose and differentiates it from sibling tools like validate_deflated_sharpe, though it is phrased as a noun phrase rather than an explicit verb action.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies this tool is for track-record length and probabilistic Sharpe analysis, and the caveat that deflated Sharpe or overfitting probability is not admission or a forecast provides some boundary relative to sibling tools. However, it never explicitly states when to prefer validate_track_record over validate_deflated_sharpe, validate_overfitting, or validate_backtest_length, and it gives no scenario guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.